Modeling of product sales promotion and price discounting strategy using fuzzy logic in a retail organization

2016 ◽  
Vol 116 (8) ◽  
pp. 1418-1444 ◽  
Author(s):  
Anup Kumar ◽  
Amit Adlakha ◽  
Kampan Mukherjee
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Data ◽  
Product Category ◽  
Sales Promotion ◽  
Series Data ◽  
Research Approach ◽  
Valuable Insight ◽  
Content Type ◽  
Product Sales

Purpose The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization. Design/methodology/approach Time series data relating to sales has been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products and selection of suppliers. A hybrid model has been proposed and explained with a hypothetical case, which considerably impacts the sales promotion and intelligent pricing decisions. Findings A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. The model imitates sales promotion and price discounting strategy. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact. Research limitations/implications There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories. Originality/value The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

A conceptual model for automation of product dynamic pricing and sales promotion for a retail organization

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 490-507 ◽  
Author(s):  
Anup Kumar
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Data ◽  
Lot Sizing ◽  
Product Category ◽  
Sales Promotion ◽  
Series Data ◽  
Valuable Insight ◽  
Content Type ◽  
Sales Forecast

Purpose – The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization. A modest attempt has been made in the study to capture the relationship between the sales promotion, price discount and the batch procurement strategy of a particular product category to maximize sales volume and profitability. Design/methodology/approach – Time series data relating to sales have been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products that considerably impact the sales promotion and intelligent pricing decisions. A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. Findings – The model captures the lag effect of sales promotion and price discounting strategy; other strategies have been formulated based upon the sales forecast that was done for taking the lot sizing decisions regarding procurement of products in the selected category. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact. Research limitations/implications – There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories. Originality/value – The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

Exploring the dynamic contribution behavior of editors in wikis based on time series analysis

2016 ◽  
Vol 50 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Linghe Huang ◽  
Qinghua Zhu ◽  
Jia Tina Du ◽  
Baozhen Lee
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Time Of Day ◽  
Observation Time ◽  
Series Data ◽  
Clustering Methods ◽  
Content Type ◽  
Free Rider Problem ◽  
Series Analysis

Purpose – Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis. Design/methodology/approach – After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia. Findings – There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions. Originality/value – By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.

An improved morphological weighted dynamic similarity measurement algorithm for time series data

2018 ◽  
Vol 11 (4) ◽  
pp. 486-495
Author(s):  
Ke Yi Zhou ◽  
Shaolin Hu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Morphological Changes ◽  
Series Data ◽  
Similarity Measurement ◽  
Improved Method ◽  
Control Measurement ◽  
Content Type ◽  
Basic Work ◽  
Measurement Algorithm

Purpose The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate. Design/methodology/approach Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system. Findings The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement. Practical implications This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis. Originality/value In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

Impact assessment of political administrations on the performance of the construction sector: a time series analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Najimu Saka ◽  
Abdullahi Babatunde Saka ◽  
Opeoluwa Akinradewo ◽  
Clinton O. Aigbavboa
Keyword(s):  
Time Series ◽  
Growth And Development ◽  
Time Series Data ◽  
Critical Factor ◽  
Sustainable Growth ◽  
Analysis Of Covariance ◽  
Series Data ◽  
Construction Sector ◽  
Content Type ◽  
Ancova Model

Purpose The complex interaction of politics and the economy is a critical factor for the sustainable growth and development of the construction sector (CNS). This study aims to investigate the effects of type of political administration including democracy and military on the performance of CNS using the Nigerian Construction Sector (NCS) as a case study. Design/methodology/approach A 48 year (1970–2017) time series data (TSD) on the NCS and the gross domestic product (GDP) based on 2010 constant USD were extracted from the United Nations Statistical Department database. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models were used to analyze the TSD. The ANCOVA model includes the GDP as correlational variable or covariate. Findings The estimates of the ANOVA model indicate that democratic administration is significantly better than military administration in construction performance. However, the ANCOVA model indicates that the GDP is more important than political administration in the performance of the CNS. The study recommends for a new national construction policy, favourable fiscal and monetary policy, local content development policy and construction credit guaranty scheme for the rapid growth and development of the NCS. Originality/value Hitherto, little is known about the influence of political administration on the performance of the CNS. This study provides empirical evidence from a developing economy perspective. It presents the relationships and highlights recommendations for driving growth in the construction industry.

Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Halim ◽  
Shuhaida Mohamed Shuhidan ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Gated Recurrent Unit

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

A history of university income in the United Kingdom and Australia, 1922–2017

2020 ◽  
Vol 49 (2) ◽  
pp. 229-248
Author(s):  
Tamson Pietsch
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Content Type ◽  
The United Kingdom ◽  
History Of University ◽  
History Of ◽  
Strategic Influence ◽  
The Uk ◽  
Comparable Time

PurposeThe purpose of this paper is to create comparable time series data on university income in Australia and the UK that might be used as a resource for those seeking to understand the changing funding profile of universities in the two countries and for those seeking to investigate how such data were produced and utilised.Design/methodology/approachA statistical analysis of university income from all sources in the UK and Australia.FindingsThe article produces a new time series for Australia and a comparable time series for the UK. It suggests some of the ways these data related to broader patterns of economic change, sketches the possibility of strategic influence, and outlines some of their limitations.Originality/valueThis is the first study to systematically create a time series on Australian university income across the twentieth century and present it alongside a comparable dataset for the UK.

Influence of systemic banking crisis and currency crisis on the relationship of export and economic growth

2017 ◽  
Vol 10 (1) ◽  
pp. 82-110
Author(s):  
Syed Ali Raza ◽  
Mohd Zaini Abd Karim
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Financial Crises ◽  
Currency Crises ◽  
Time Series Data ◽  
Banking Crises ◽  
Series Data ◽  
Content Type ◽  
Relationship Of ◽  
The Relationship

Purpose This study aims to investigate the influence of systemic banking crises, currency crises and global financial crisis on the relationship between export and economic growth in China by using the annual time series data from the period of 1972 to 2014. Design/methodology/approach The Johansen and Jeuuselius’ cointegration, auto regressive distributed lag bound testing cointegration, Gregory and Hansen’s cointegration and pooled ordinary least square techniques with error correction model have been used. Findings Results indicate the positive and significant effect of export of goods and services on economic growth in both long and short run, whereas the negative influence of systemic banking crises and currency crises over economic growth is observed. It is also concluded that the impact of export of goods and service on economic growth becomes insignificant in the presence of systemic banking crises and currency crises. The currency crises effect the influence of export on economic growth to a higher extent compared to systemic banking crises. Surprisingly, the export in the period of global financial crises has a positive and significant influence over economic growth in China, which conclude that the global financial crises did not drastically affect the export-growth nexus. Originality/value This paper makes a unique contribution to the literature with reference to China, being a pioneering attempt to investigate the effects of systemic banking crises and currency crises on the relationship of export and economic growth by using long-time series data and applying more rigorous econometric techniques.

Analysing and forecasting China containerized freight index with a hybrid decomposition–ensemble method based on EMD, grey wave and ARMA

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanhui Chen ◽  
Bin Liu ◽  
Tianzi Wang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Grey System Theory ◽  
Ensemble Method ◽  
Series Data ◽  
Forecasting Model ◽  
Content Type ◽  
Forecasting Method ◽  
Periodic Fluctuations ◽  
Wave Forecasting

PurposeThis paper applied grey wave forecasting in a decomposition–ensemble forecasting method for modelling the complex and non-linear features in time series data. This application aims to test the advantages of grey wave forecasting method in predicting time series with periodic fluctuations.Design/methodology/approachThe decomposition–ensemble method combines empirical mode decomposition (EMD), component reconstruction technology and grey wave forecasting. More specifically, EMD is used to decompose time series data into different intrinsic mode function (IMF) components in the first step. Permutation entropy and the average of each IMF are checked for component reconstruction. Then the grey wave forecasting model or ARMA is used to predict each IMF according to the characters of each IMF.FindingsIn the empirical analysis, the China container freight index (CCFI) is applied in checking prediction performance. Using two different time periods, the results show that the proposed method performs better than random walk and ARMA in multi-step-ahead prediction.Originality/valueThe decomposition–ensemble method based on EMD and grey wave forecasting model expands the application area of the grey system theory and graphic forecasting method. Grey wave forecasting performs better for data set with periodic fluctuations. Forecasting CCFI assists practitioners in the shipping industry in decision-making.

FDI and economic growth: empirical evidence from Pakistan

2016 ◽  
Vol 32 (1) ◽  
pp. 63-76 ◽  
Author(s):  
Naqeeb Ur Rehman
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Human Capital ◽  
Time Series Data ◽  
Negative Relationship ◽  
Series Data ◽  
Literacy Rate ◽  
Content Type ◽  
Low Level ◽  
The Relationship

Purpose – The purpose of this paper is to investigate the relationship between FDI and economic growth. Two models have been used to analyse the time series data on Pakistan from 1970 to 2012. This paper contributes to the existing literature by examining the different empirical methods to estimate the relationship between FDI and economic growth. The vector error correction model (VECM) results suggest that FDI depends on the economic growth but this relationship is not true vice versa. The second model showed that FDI, human capital and exports are important factors of economic growth. However, the negative relationship between interactive variables (FDI and human capital) and economic growth indicates that low level of human capital affect the economic growth of Pakistan. Design/methodology/approach – Used time series data (1970-2012) for empirical analysis. Findings – The VECM results suggest that FDI depends on the economic growth but this relationship is not true vice versa. The second model showed that FDI, human capital and exports are important factors of economic growth. However, the negative relationship between interactive variables (FDI and human capital) and economic growth indicates that low level of human capital affect the economic growth of Pakistan. Research limitations/implications – The limitations of this empirical paper are as follows: it would be better to use secondary school enrolment (per cent) to measure human capital instead adult literacy rate. Similarly, the non-availability of R & D data on Pakistan limited the scope of the paper to measure the role of absorptive capacity of domestic and its relationship with FDI. The results of this paper are specifically related to Pakistan and cannot be generalized to other countries. Practical implications – This empirical study implies that Pakistan should improve its economic growth. The robust policies are required to increase the literacy rate of the country. Higher human capital will attract more FDI into the economy and may reduce the unemployment. This would increase the national output of the country and their national income level. Presently, Pakistan is going through war on terror and foreign firms are reluctant to invest. A stable and secure business environment will ultimately inject foreign direct investment into Pakistan. Originality/value – This paper is first time analyse the time series data to explore the relationship between FDI and economic growth. A new approach has been used called VECM.

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